mirror of
https://github.com/clearml/clearml
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Added text classification example and updated image and audio examples
This commit is contained in:
danmalowany-allegro
parent
8cb7c8130a
commit
10f11ba038
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "e-YsQrBjzNdX"
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},
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"outputs": [],
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"source": [
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"! pip install -U pip\n",
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"! pip install -U torch==1.5.0\n",
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"! pip install -U torchaudio==0.5.0\n",
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"! pip install -U torchvision==0.6.0\n",
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"! pip install -U matplotlib==3.2.1\n",
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"! pip install -U trains>=0.15.0\n",
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"! pip install -U pandas==1.0.4\n",
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"! pip install -U numpy==1.18.4\n",
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"! pip install -U tensorboard==2.2.1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "T7T0Rf26zNdm"
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},
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"outputs": [],
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"source": [
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"import PIL\n",
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"import io\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from pathlib2 import Path\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"import torch.optim as optim\n",
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"from torch.utils.data import Dataset\n",
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"from torch.utils.tensorboard import SummaryWriter\n",
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"\n",
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"import torchaudio\n",
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"from torchvision.transforms import ToTensor\n",
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"\n",
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"from trains import Task\n",
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"\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"task = Task.init(project_name='Audio Example', task_name='audio classifier')\n",
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"configuration_dict = {'number_of_epochs': 10, 'batch_size': 4, 'dropout': 0.25, 'base_lr': 0.001}\n",
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"configuration_dict = task.connect(configuration_dict) # enabling configuration override by trains\n",
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"print(configuration_dict) # printing actual configuration (after override in remote mode)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "msiz7QdvzNeA",
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# Download UrbanSound8K dataset (https://urbansounddataset.weebly.com/urbansound8k.html)\n",
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"path_to_UrbanSound8K = './data/UrbanSound8K'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "wXtmZe7yzNeS"
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},
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"outputs": [],
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"source": [
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"class UrbanSoundDataset(Dataset):\n",
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"#rapper for the UrbanSound8K dataset\n",
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" def __init__(self, csv_path, file_path, folderList):\n",
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" self.file_path = file_path\n",
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" self.file_names = []\n",
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" self.labels = []\n",
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" self.folders = []\n",
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" \n",
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" #loop through the csv entries and only add entries from folders in the folder list\n",
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" csvData = pd.read_csv(csv_path)\n",
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" for i in range(0,len(csvData)):\n",
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" if csvData.iloc[i, 5] in folderList:\n",
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" self.file_names.append(csvData.iloc[i, 0])\n",
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" self.labels.append(csvData.iloc[i, 6])\n",
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" self.folders.append(csvData.iloc[i, 5])\n",
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" \n",
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" def __getitem__(self, index):\n",
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" #format the file path and load the file\n",
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" path = self.file_path / (\"fold\" + str(self.folders[index])) / self.file_names[index]\n",
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" sound, sample_rate = torchaudio.load(path, out = None, normalization = True)\n",
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"\n",
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" # UrbanSound8K uses two channels, this will convert them to one\n",
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" soundData = torch.mean(sound, dim=0, keepdim=True)\n",
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" \n",
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" #Make sure all files are the same size\n",
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" if soundData.numel() < 160000:\n",
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" fixedsize_data = torch.nn.functional.pad(soundData, (0, 160000 - soundData.numel()))\n",
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" else:\n",
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" fixedsize_data = soundData[0,:160000].reshape(1,160000)\n",
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" \n",
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" #downsample the audio\n",
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" downsample_data = fixedsize_data[::5]\n",
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" \n",
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" melspectogram_transform = torchaudio.transforms.MelSpectrogram(sample_rate=sample_rate)\n",
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" melspectogram = melspectogram_transform(downsample_data)\n",
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" melspectogram_db = torchaudio.transforms.AmplitudeToDB()(melspectogram)\n",
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"\n",
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" return fixedsize_data, sample_rate, melspectogram_db, self.labels[index]\n",
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" \n",
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" def __len__(self):\n",
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" return len(self.file_names)\n",
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"\n",
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"\n",
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"csv_path = Path(path_to_UrbanSound8K) / 'metadata' / 'UrbanSound8K.csv'\n",
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"file_path = Path(path_to_UrbanSound8K) / 'audio'\n",
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"\n",
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"train_set = UrbanSoundDataset(csv_path, file_path, range(1,10))\n",
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"test_set = UrbanSoundDataset(csv_path, file_path, [10])\n",
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"print(\"Train set size: \" + str(len(train_set)))\n",
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"print(\"Test set size: \" + str(len(test_set)))\n",
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"\n",
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"train_loader = torch.utils.data.DataLoader(train_set, batch_size = configuration_dict.get('batch_size', 4), \n",
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" shuffle = True, pin_memory=True, num_workers=1)\n",
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"test_loader = torch.utils.data.DataLoader(test_set, batch_size = configuration_dict.get('batch_size', 4), \n",
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" shuffle = False, pin_memory=True, num_workers=1)\n",
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"\n",
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"classes = ('air_conditioner', 'car_horn', 'children_playing', 'dog_bark', 'drilling', 'engine_idling', \n",
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" 'gun_shot', 'jackhammer', 'siren', 'street_music')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "ylblw-k1zNeZ"
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},
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"outputs": [],
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"source": [
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"class Net(nn.Module):\n",
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" def __init__(self, num_classes, dropout_value):\n",
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" super(Net,self).__init__()\n",
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" self.num_classes = num_classes\n",
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" self.dropout_value = dropout_value\n",
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" \n",
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" self.C1 = nn.Conv2d(1,16,3)\n",
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" self.C2 = nn.Conv2d(16,32,3)\n",
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" self.C3 = nn.Conv2d(32,64,3)\n",
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" self.C4 = nn.Conv2d(64,128,3)\n",
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" self.maxpool1 = nn.MaxPool2d(2,2) \n",
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" self.fc1 = nn.Linear(128*29*197,128)\n",
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" self.fc2 = nn.Linear(128,self.num_classes)\n",
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" self.dropout = nn.Dropout(self.dropout_value)\n",
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" \n",
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" def forward(self,x):\n",
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" # add sequence of convolutional and max pooling layers\n",
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" x = F.relu(self.C1(x))\n",
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" x = self.maxpool1(F.relu(self.C2(x)))\n",
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" x = F.relu(self.C3(x))\n",
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" x = self.maxpool1(F.relu(self.C4(x)))\n",
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" # flatten image input\n",
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" x = x.view(-1,128*29*197)\n",
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" x = F.relu(self.fc1(self.dropout(x)))\n",
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" x = self.fc2(self.dropout(x))\n",
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" return x\n",
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" \n",
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" \n",
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"model = Net(len(classes), configuration_dict.get('dropout', 0.25))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "3yKYru14zNef"
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},
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"outputs": [],
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"source": [
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"optimizer = optim.SGD(model.parameters(), lr = configuration_dict.get('base_lr', 0.001), momentum = 0.9)\n",
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"scheduler = optim.lr_scheduler.StepLR(optimizer, step_size = 3, gamma = 0.1)\n",
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"criterion = nn.CrossEntropyLoss()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"device = torch.cuda.current_device() if torch.cuda.is_available() else torch.device('cpu')\n",
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"print('Device to use: {}'.format(device))\n",
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"model.to(device)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"tensorboard_writer = SummaryWriter('./tensorboard_logs')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_signal(signal, title, cmap=None):\n",
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" fig = plt.figure()\n",
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" if signal.ndim == 1:\n",
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" plt.plot(signal)\n",
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" else:\n",
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" plt.imshow(signal, cmap=cmap) \n",
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" plt.title(title)\n",
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" \n",
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" plot_buf = io.BytesIO()\n",
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" plt.savefig(plot_buf, format='jpeg')\n",
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" plot_buf.seek(0)\n",
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" plt.close(fig)\n",
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" return ToTensor()(PIL.Image.open(plot_buf))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "Vdthqz3JzNem"
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},
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"outputs": [],
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"source": [
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"def train(model, epoch):\n",
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" model.train()\n",
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" for batch_idx, (sounds, sample_rate, inputs, labels) in enumerate(train_loader):\n",
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" inputs = inputs.to(device)\n",
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" labels = labels.to(device)\n",
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"\n",
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" # zero the parameter gradients\n",
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" optimizer.zero_grad()\n",
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"\n",
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" # forward + backward + optimize\n",
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" outputs = model(inputs)\n",
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" _, predicted = torch.max(outputs, 1)\n",
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" loss = criterion(outputs, labels)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" \n",
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" iteration = epoch * len(train_loader) + batch_idx\n",
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" if batch_idx % log_interval == 0: #print training stats\n",
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" print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'\n",
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" .format(epoch, batch_idx * len(inputs), len(train_loader.dataset), \n",
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" 100. * batch_idx / len(train_loader), loss))\n",
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" tensorboard_writer.add_scalar('training loss/loss', loss, iteration)\n",
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" tensorboard_writer.add_scalar('learning rate/lr', optimizer.param_groups[0]['lr'], iteration)\n",
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" \n",
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" \n",
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" if batch_idx % debug_interval == 0: # report debug image every \"debug_interval\" mini-batches\n",
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" for n, (inp, pred, label) in enumerate(zip(inputs, predicted, labels)):\n",
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" series = 'label_{}_pred_{}'.format(classes[label.cpu()], classes[pred.cpu()])\n",
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" tensorboard_writer.add_image('Train MelSpectrogram samples/{}_{}_{}'.format(batch_idx, n, series), \n",
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" plot_signal(inp.cpu().numpy().squeeze(), series, 'hot'), iteration)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "LBWoj7u5zNes"
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},
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"outputs": [],
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"source": [
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"def test(model, epoch):\n",
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" model.eval()\n",
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" class_correct = list(0. for i in range(10))\n",
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" class_total = list(0. for i in range(10))\n",
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" with torch.no_grad():\n",
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" for idx, (sounds, sample_rate, inputs, labels) in enumerate(test_loader):\n",
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" inputs = inputs.to(device)\n",
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" labels = labels.to(device)\n",
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"\n",
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" outputs = model(inputs)\n",
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"\n",
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" _, predicted = torch.max(outputs, 1)\n",
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" c = (predicted == labels)\n",
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" for i in range(len(inputs)):\n",
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" label = labels[i].item()\n",
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" class_correct[label] += c[i].item()\n",
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" class_total[label] += 1\n",
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" \n",
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" iteration = (epoch + 1) * len(train_loader)\n",
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" if idx % debug_interval == 0: # report debug image every \"debug_interval\" mini-batches\n",
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" for n, (sound, inp, pred, label) in enumerate(zip(sounds, inputs, predicted, labels)):\n",
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" series = 'label_{}_pred_{}'.format(classes[label.cpu()], classes[pred.cpu()])\n",
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" tensorboard_writer.add_audio('Test audio samples/{}_{}_{}'.format(idx, n, series), \n",
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" sound, iteration, int(sample_rate[n]))\n",
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" tensorboard_writer.add_image('Test MelSpectrogram samples/{}_{}_{}'.format(idx, n, series), \n",
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" plot_signal(inp.cpu().numpy().squeeze(), series, 'hot'), iteration)\n",
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"\n",
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" total_accuracy = 100 * sum(class_correct)/sum(class_total)\n",
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" print('[Iteration {}] Accuracy on the {} test images: {}%\\n'.format(epoch, sum(class_total), total_accuracy))\n",
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" tensorboard_writer.add_scalar('accuracy/total', total_accuracy, iteration)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "X5lx3g_5zNey",
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"log_interval = 100\n",
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"debug_interval = 200\n",
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"for epoch in range(configuration_dict.get('number_of_epochs', 10)):\n",
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" train(model, epoch)\n",
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" test(model, epoch)\n",
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" scheduler.step()"
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]
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}
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],
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"metadata": {
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"colab": {
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"name": "audio_classifier_tutorial.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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@ -0,0 +1,317 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install -U pip\n",
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"! pip install -U torch==1.5.0\n",
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"! pip install -U torchtext==0.6.0\n",
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"! pip install -U matplotlib==3.2.1\n",
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"! pip install -U trains>=0.15.0\n",
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"! pip install -U tensorboard==2.2.1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import time\n",
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"\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.nn.functional as F\n",
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"import torchtext\n",
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"from torchtext.datasets import text_classification\n",
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"from torch.utils.data import DataLoader\n",
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"from torch.utils.tensorboard import SummaryWriter\n",
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"\n",
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"from trains import Task\n",
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"\n",
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"%matplotlib inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"task = Task.init(project_name='Text Example', task_name='text classifier')\n",
|
||||
"configuration_dict = {'number_of_epochs': 6, 'batch_size': 16, 'ngrams': 2, 'base_lr': 1.0}\n",
|
||||
"configuration_dict = task.connect(configuration_dict) # enabling configuration override by trains\n",
|
||||
"print(configuration_dict) # printing actual configuration (after override in remote mode)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if not os.path.isdir('./data'):\n",
|
||||
" os.mkdir('./data')\n",
|
||||
"train_dataset, test_dataset = text_classification.DATASETS['AG_NEWS'](root='./data', \n",
|
||||
" ngrams=configuration_dict.get('ngrams', 2))\n",
|
||||
"vocabulary = train_dataset.get_vocab()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def generate_batch(batch):\n",
|
||||
" label = torch.tensor([entry[0] for entry in batch])\n",
|
||||
" # original data batch input are packed into a list and concatenated as a single tensor\n",
|
||||
" text = [entry[1] for entry in batch]\n",
|
||||
" # offsets is a tensor of delimiters to represent the beginning index of each sequence in the text tensor.\n",
|
||||
" offsets = [0] + [len(entry) for entry in text] \n",
|
||||
" \n",
|
||||
" # torch.Tensor.cumsum returns the cumulative sum of elements in the dimension dim.\n",
|
||||
" offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)\n",
|
||||
" text = torch.cat(text)\n",
|
||||
" return text, offsets, label\n",
|
||||
"\n",
|
||||
"train_loader = torch.utils.data.DataLoader(train_dataset, batch_size = configuration_dict.get('batch_size', 16), \n",
|
||||
" shuffle = True, pin_memory=True, collate_fn=generate_batch)\n",
|
||||
"test_loader = torch.utils.data.DataLoader(test_dataset, batch_size = configuration_dict.get('batch_size', 16), \n",
|
||||
" shuffle = False, pin_memory=True, collate_fn=generate_batch)\n",
|
||||
"\n",
|
||||
"classes = (\"World\", \"Sports\", \"Business\", \"Sci/Tec\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class TextSentiment(nn.Module):\n",
|
||||
" def __init__(self, vocab_size, embed_dim, num_class):\n",
|
||||
" super().__init__()\n",
|
||||
" self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)\n",
|
||||
" self.fc = nn.Linear(embed_dim, num_class)\n",
|
||||
" self.init_weights()\n",
|
||||
"\n",
|
||||
" def init_weights(self):\n",
|
||||
" initrange = 0.5\n",
|
||||
" self.embedding.weight.data.uniform_(-initrange, initrange)\n",
|
||||
" self.fc.weight.data.uniform_(-initrange, initrange)\n",
|
||||
" self.fc.bias.data.zero_()\n",
|
||||
"\n",
|
||||
" def forward(self, text, offsets):\n",
|
||||
" embedded = self.embedding(text, offsets)\n",
|
||||
" return self.fc(embedded)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"VOCAB_SIZE = len(train_dataset.get_vocab())\n",
|
||||
"EMBED_DIM = 32\n",
|
||||
"NUN_CLASS = len(train_dataset.get_labels())\n",
|
||||
"model = TextSentiment(VOCAB_SIZE, EMBED_DIM, NUN_CLASS)\n",
|
||||
"\n",
|
||||
"device = torch.cuda.current_device() if torch.cuda.is_available() else torch.device('cpu')\n",
|
||||
"print('Device to use: {}'.format(device))\n",
|
||||
"model.to(device)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"criterion = torch.nn.CrossEntropyLoss().to(device)\n",
|
||||
"optimizer = torch.optim.SGD(model.parameters(), lr=configuration_dict.get('base_lr', 1.0))\n",
|
||||
"scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 2, gamma=0.9)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tensorboard_writer = SummaryWriter('./tensorboard_logs')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def train_func(data, epoch):\n",
|
||||
" # Train the model\n",
|
||||
" train_loss = 0\n",
|
||||
" train_acc = 0\n",
|
||||
" for batch_idx, (text, offsets, cls) in enumerate(data):\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" text, offsets, cls = text.to(device), offsets.to(device), cls.to(device)\n",
|
||||
" output = model(text, offsets)\n",
|
||||
" loss = criterion(output, cls)\n",
|
||||
" train_loss += loss.item()\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()\n",
|
||||
" train_acc += (output.argmax(1) == cls).sum().item()\n",
|
||||
" \n",
|
||||
" iteration = epoch * len(train_loader) + batch_idx\n",
|
||||
" if batch_idx % log_interval == 0: \n",
|
||||
" print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'\n",
|
||||
" .format(epoch, batch_idx * len(cls), len(train_dataset), \n",
|
||||
" 100. * batch_idx / len(train_loader), loss))\n",
|
||||
" tensorboard_writer.add_scalar('training loss/loss', loss, iteration)\n",
|
||||
" tensorboard_writer.add_scalar('learning rate/lr', optimizer.param_groups[0]['lr'], iteration)\n",
|
||||
"\n",
|
||||
" # Adjust the learning rate\n",
|
||||
" scheduler.step()\n",
|
||||
"\n",
|
||||
" return train_loss / len(train_dataset), train_acc / len(train_dataset)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def test(data, epoch):\n",
|
||||
" loss = 0\n",
|
||||
" acc = 0\n",
|
||||
" for idx, (text, offsets, cls) in enumerate(data):\n",
|
||||
" text, offsets, cls = text.to(device), offsets.to(device), cls.to(device)\n",
|
||||
" with torch.no_grad():\n",
|
||||
" output = model(text, offsets)\n",
|
||||
" predicted = output.argmax(1)\n",
|
||||
" loss = criterion(output, cls)\n",
|
||||
" loss += loss.item()\n",
|
||||
" acc += (predicted == cls).sum().item()\n",
|
||||
" \n",
|
||||
" iteration = (epoch + 1) * len(train_loader)\n",
|
||||
" if idx % debug_interval == 0: # report debug text every \"debug_interval\" mini-batches\n",
|
||||
" offsets = offsets.tolist() + [len(text)]\n",
|
||||
" for n, (pred, label) in enumerate(zip(predicted, cls)):\n",
|
||||
" ids_to_text = [vocabulary.itos[id] for id in text[offsets[n]:offsets[n+1]]]\n",
|
||||
" series = '{}_{}_label_{}_pred_{}'.format(idx, n, classes[label], classes[pred])\n",
|
||||
" tensorboard_writer.add_text('Test text samples/{}'.format(series), \n",
|
||||
" ' '.join(ids_to_text), iteration)\n",
|
||||
"\n",
|
||||
" return loss / len(test_dataset), acc / len(test_dataset)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"log_interval = 200\n",
|
||||
"debug_interval = 500\n",
|
||||
"for epoch in range(configuration_dict.get('number_of_epochs', 6)):\n",
|
||||
" start_time = time.time()\n",
|
||||
" \n",
|
||||
" train_loss, train_acc = train_func(train_loader, epoch)\n",
|
||||
" test_loss, test_acc = test(test_loader, epoch)\n",
|
||||
" \n",
|
||||
" secs = int(time.time() - start_time)\n",
|
||||
"\n",
|
||||
" print('Epoch: %d' %(epoch + 1), \" | time in %d minutes, %d seconds\" %(secs / 60, secs % 60))\n",
|
||||
" print(f'\\tLoss: {train_loss:.4f}(train)\\t|\\tAcc: {train_acc * 100:.1f}%(train)')\n",
|
||||
" print(f'\\tLoss: {test_loss:.4f}(test)\\t|\\tAcc: {test_acc * 100:.1f}%(test)')\n",
|
||||
" tensorboard_writer.add_scalar('accuracy/train', train_acc, (epoch + 1) * len(train_loader))\n",
|
||||
" tensorboard_writer.add_scalar('accuracy/test', test_acc, (epoch + 1) * len(train_loader))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import re\n",
|
||||
"from torchtext.data.utils import ngrams_iterator\n",
|
||||
"from torchtext.data.utils import get_tokenizer\n",
|
||||
"\n",
|
||||
"def predict(text, model, vocab, ngrams):\n",
|
||||
" tokenizer = get_tokenizer(\"basic_english\")\n",
|
||||
" with torch.no_grad():\n",
|
||||
" text = torch.tensor([vocab[token]\n",
|
||||
" for token in ngrams_iterator(tokenizer(text), ngrams)])\n",
|
||||
" output = model(text, torch.tensor([0]))\n",
|
||||
" return output.argmax(1).item()\n",
|
||||
"\n",
|
||||
"ex_text_str = \"MEMPHIS, Tenn. – Four days ago, Jon Rahm was \\\n",
|
||||
" enduring the season’s worst weather conditions on Sunday at The \\\n",
|
||||
" Open on his way to a closing 75 at Royal Portrush, which \\\n",
|
||||
" considering the wind and the rain was a respectable showing. \\\n",
|
||||
" Thursday’s first round at the WGC-FedEx St. Jude Invitational \\\n",
|
||||
" was another story. With temperatures in the mid-80s and hardly any \\\n",
|
||||
" wind, the Spaniard was 13 strokes better in a flawless round. \\\n",
|
||||
" Thanks to his best putting performance on the PGA Tour, Rahm \\\n",
|
||||
" finished with an 8-under 62 for a three-stroke lead, which \\\n",
|
||||
" was even more impressive considering he’d never played the \\\n",
|
||||
" front nine at TPC Southwind.\"\n",
|
||||
"\n",
|
||||
"ans = predict(ex_text_str, model.to(\"cpu\"), vocabulary, configuration_dict.get('ngrams', 2))\n",
|
||||
"print(\"This is a %s news\" %classes[ans])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user